Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations8898
Missing cells244
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.5 MiB
Average record size in memory760.4 B

Variable types

Numeric4
Categorical5
Text7
DateTime1

Alerts

CircuitID is highly overall correlated with RoundHigh correlation
ConstructorID is highly overall correlated with ConstructorName and 1 other fieldsHigh correlation
ConstructorName is highly overall correlated with ConstructorID and 1 other fieldsHigh correlation
ConstructorNationality is highly overall correlated with ConstructorID and 1 other fieldsHigh correlation
Nationality is highly overall correlated with PermanentNumberHigh correlation
PermanentNumber is highly overall correlated with Nationality and 1 other fieldsHigh correlation
Round is highly overall correlated with CircuitIDHigh correlation
Season is highly overall correlated with PermanentNumberHigh correlation
Code has 244 (2.7%) missing valuesMissing
PermanentNumber has 2661 (29.9%) zerosZeros

Reproduction

Analysis started2024-08-13 00:02:44.699751
Analysis finished2024-08-13 00:02:47.425400
Duration2.73 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

Season
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.4229
Minimum2000
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2024-08-12T21:02:47.494222image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2003
Q12008
median2013
Q32019
95-th percentile2023
Maximum2024
Range24
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.3321536
Coefficient of variation (CV)0.0031449695
Kurtosis-1.0907907
Mean2013.4229
Median Absolute Deviation (MAD)5
Skewness-0.070296226
Sum17915437
Variance40.096169
MonotonicityIncreasing
2024-08-12T21:02:47.630554image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2012 476
 
5.3%
2016 457
 
5.1%
2010 456
 
5.1%
2011 452
 
5.1%
2022 440
 
4.9%
2023 440
 
4.9%
2021 439
 
4.9%
2018 420
 
4.7%
2019 418
 
4.7%
2013 418
 
4.7%
Other values (15) 4482
50.4%
ValueCountFrequency (%)
2000 88
 
1.0%
2001 22
 
0.2%
2002 42
 
0.5%
2003 320
3.6%
2004 360
4.0%
2005 376
4.2%
2006 396
4.5%
2007 374
4.2%
2008 368
4.1%
2009 340
3.8%
ValueCountFrequency (%)
2024 279
3.1%
2023 440
4.9%
2022 440
4.9%
2021 439
4.9%
2020 340
3.8%
2019 418
4.7%
2018 420
4.7%
2017 398
4.5%
2016 457
5.1%
2015 374
4.2%

Round
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.063497
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2024-08-12T21:02:47.763970image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median10
Q315
95-th percentile19
Maximum22
Range21
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.6762444
Coefficient of variation (CV)0.56404292
Kurtosis-1.1014975
Mean10.063497
Median Absolute Deviation (MAD)5
Skewness0.094492333
Sum89545
Variance32.219751
MonotonicityNot monotonic
2024-08-12T21:02:47.994580image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2 485
 
5.5%
1 484
 
5.4%
4 484
 
5.4%
16 484
 
5.4%
3 483
 
5.4%
9 462
 
5.2%
11 462
 
5.2%
14 461
 
5.2%
12 461
 
5.2%
10 461
 
5.2%
Other values (12) 4171
46.9%
ValueCountFrequency (%)
1 484
5.4%
2 485
5.5%
3 483
5.4%
4 484
5.4%
5 458
5.1%
6 459
5.2%
7 460
5.2%
8 460
5.2%
9 462
5.2%
10 461
5.2%
ValueCountFrequency (%)
22 60
 
0.7%
21 122
 
1.4%
20 166
 
1.9%
19 293
3.3%
18 354
4.0%
17 438
4.9%
16 484
5.4%
15 441
5.0%
14 461
5.2%
13 460
5.2%

CircuitID
Categorical

HIGH CORRELATION 

Distinct38
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size498.4 KiB
silverstone
 
503
hungaroring
 
462
catalunya
 
461
albert_park
 
443
monza
 
440
Other values (33)
6589 

Length

Max length14
Median length12
Mean length8.3417622
Min length3

Characters and Unicode

Total characters74225
Distinct characters24
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowalbert_park
2nd rowalbert_park
3rd rowalbert_park
4th rowalbert_park
5th rowalbert_park

Common Values

ValueCountFrequency (%)
silverstone 503
 
5.7%
hungaroring 462
 
5.2%
catalunya 461
 
5.2%
albert_park 443
 
5.0%
monza 440
 
4.9%
bahrain 439
 
4.9%
monaco 438
 
4.9%
suzuka 422
 
4.7%
spa 418
 
4.7%
interlagos 418
 
4.7%
Other values (28) 4454
50.1%

Length

2024-08-12T21:02:48.116256image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
silverstone 503
 
5.7%
hungaroring 462
 
5.2%
catalunya 461
 
5.2%
albert_park 443
 
5.0%
monza 440
 
4.9%
bahrain 439
 
4.9%
monaco 438
 
4.9%
suzuka 422
 
4.7%
interlagos 418
 
4.7%
spa 418
 
4.7%
Other values (28) 4454
50.1%

Most occurring characters

ValueCountFrequency (%)
a 10682
14.4%
n 7302
 
9.8%
r 6083
 
8.2%
i 5869
 
7.9%
e 4874
 
6.6%
s 4171
 
5.6%
o 4042
 
5.4%
l 3925
 
5.3%
u 3579
 
4.8%
g 3362
 
4.5%
Other values (14) 20336
27.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 72479
97.6%
Connector Punctuation 1746
 
2.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 10682
14.7%
n 7302
 
10.1%
r 6083
 
8.4%
i 5869
 
8.1%
e 4874
 
6.7%
s 4171
 
5.8%
o 4042
 
5.6%
l 3925
 
5.4%
u 3579
 
4.9%
g 3362
 
4.6%
Other values (13) 18590
25.6%
Connector Punctuation
ValueCountFrequency (%)
_ 1746
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 72479
97.6%
Common 1746
 
2.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 10682
14.7%
n 7302
 
10.1%
r 6083
 
8.4%
i 5869
 
8.1%
e 4874
 
6.7%
s 4171
 
5.8%
o 4042
 
5.6%
l 3925
 
5.4%
u 3579
 
4.9%
g 3362
 
4.6%
Other values (13) 18590
25.6%
Common
ValueCountFrequency (%)
_ 1746
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74225
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 10682
14.4%
n 7302
 
9.8%
r 6083
 
8.2%
i 5869
 
7.9%
e 4874
 
6.6%
s 4171
 
5.6%
o 4042
 
5.4%
l 3925
 
5.3%
u 3579
 
4.8%
g 3362
 
4.5%
Other values (14) 20336
27.4%

Position
Real number (ℝ)

Distinct24
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.020004
Minimum1
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2024-08-12T21:02:48.223464image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median11
Q316
95-th percentile20
Maximum24
Range23
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.1297869
Coefficient of variation (CV)0.55624178
Kurtosis-1.1203658
Mean11.020004
Median Absolute Deviation (MAD)5
Skewness0.0552226
Sum98056
Variance37.574288
MonotonicityNot monotonic
2024-08-12T21:02:48.335745image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 425
 
4.8%
11 425
 
4.8%
18 425
 
4.8%
17 425
 
4.8%
16 425
 
4.8%
15 425
 
4.8%
14 425
 
4.8%
2 425
 
4.8%
12 425
 
4.8%
13 425
 
4.8%
Other values (14) 4648
52.2%
ValueCountFrequency (%)
1 425
4.8%
2 425
4.8%
3 425
4.8%
4 425
4.8%
5 425
4.8%
6 425
4.8%
7 425
4.8%
8 425
4.8%
9 425
4.8%
10 425
4.8%
ValueCountFrequency (%)
24 50
 
0.6%
23 58
 
0.7%
22 153
 
1.7%
21 159
 
1.8%
20 410
4.6%
19 418
4.7%
18 425
4.8%
17 425
4.8%
16 425
4.8%
15 425
4.8%
Distinct122
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size491.9 KiB
2024-08-12T21:02:48.690896image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length18
Median length15
Mean length7.5915936
Min length3

Characters and Unicode

Total characters67550
Distinct characters27
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowhakkinen
2nd rowcoulthard
3rd rowmichael_schumacher
4th rowbarrichello
5th rowfrentzen
ValueCountFrequency (%)
alonso 377
 
4.2%
hamilton 346
 
3.9%
raikkonen 321
 
3.6%
vettel 299
 
3.4%
perez 272
 
3.1%
button 263
 
3.0%
massa 257
 
2.9%
ricciardo 252
 
2.8%
bottas 237
 
2.7%
hulkenberg 219
 
2.5%
Other values (112) 6055
68.0%
2024-08-12T21:02:49.055215image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 6535
 
9.7%
a 6526
 
9.7%
r 5280
 
7.8%
o 5222
 
7.7%
n 5115
 
7.6%
l 4777
 
7.1%
s 4591
 
6.8%
i 4590
 
6.8%
t 3755
 
5.6%
c 2472
 
3.7%
Other values (17) 18687
27.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 66727
98.8%
Connector Punctuation 823
 
1.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 6535
 
9.8%
a 6526
 
9.8%
r 5280
 
7.9%
o 5222
 
7.8%
n 5115
 
7.7%
l 4777
 
7.2%
s 4591
 
6.9%
i 4590
 
6.9%
t 3755
 
5.6%
c 2472
 
3.7%
Other values (16) 17864
26.8%
Connector Punctuation
ValueCountFrequency (%)
_ 823
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 66727
98.8%
Common 823
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 6535
 
9.8%
a 6526
 
9.8%
r 5280
 
7.9%
o 5222
 
7.8%
n 5115
 
7.7%
l 4777
 
7.2%
s 4591
 
6.9%
i 4590
 
6.9%
t 3755
 
5.6%
c 2472
 
3.7%
Other values (16) 17864
26.8%
Common
ValueCountFrequency (%)
_ 823
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67550
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 6535
 
9.7%
a 6526
 
9.7%
r 5280
 
7.8%
o 5222
 
7.7%
n 5115
 
7.6%
l 4777
 
7.1%
s 4591
 
6.8%
i 4590
 
6.8%
t 3755
 
5.6%
c 2472
 
3.7%
Other values (17) 18687
27.7%

Code
Text

MISSING 

Distinct96
Distinct (%)1.1%
Missing244
Missing (%)2.7%
Memory size447.2 KiB
2024-08-12T21:02:49.272639image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters25962
Distinct characters24
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st rowCOU
2nd rowMSC
3rd rowBAR
4th rowTRU
5th rowVIL
ValueCountFrequency (%)
alo 377
 
4.4%
ham 346
 
4.0%
rai 321
 
3.7%
vet 299
 
3.5%
per 272
 
3.1%
but 263
 
3.0%
ver 257
 
3.0%
mas 257
 
3.0%
ric 252
 
2.9%
bot 237
 
2.7%
Other values (86) 5773
66.7%
2024-08-12T21:02:49.574813image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 2851
 
11.0%
R 2612
 
10.1%
O 2092
 
8.1%
S 1956
 
7.5%
I 1695
 
6.5%
E 1678
 
6.5%
T 1644
 
6.3%
L 1452
 
5.6%
U 1429
 
5.5%
B 1302
 
5.0%
Other values (14) 7251
27.9%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 25962
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 2851
 
11.0%
R 2612
 
10.1%
O 2092
 
8.1%
S 1956
 
7.5%
I 1695
 
6.5%
E 1678
 
6.5%
T 1644
 
6.3%
L 1452
 
5.6%
U 1429
 
5.5%
B 1302
 
5.0%
Other values (14) 7251
27.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 25962
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 2851
 
11.0%
R 2612
 
10.1%
O 2092
 
8.1%
S 1956
 
7.5%
I 1695
 
6.5%
E 1678
 
6.5%
T 1644
 
6.3%
L 1452
 
5.6%
U 1429
 
5.5%
B 1302
 
5.0%
Other values (14) 7251
27.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 25962
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 2851
 
11.0%
R 2612
 
10.1%
O 2092
 
8.1%
S 1956
 
7.5%
I 1695
 
6.5%
E 1678
 
6.5%
T 1644
 
6.3%
L 1452
 
5.6%
U 1429
 
5.5%
B 1302
 
5.0%
Other values (14) 7251
27.9%

PermanentNumber
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.627444
Minimum0
Maximum99
Zeros2661
Zeros (%)29.9%
Negative0
Negative (%)0.0%
Memory size69.6 KiB
2024-08-12T21:02:49.704659image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median10
Q325
95-th percentile77
Maximum99
Range99
Interquartile range (IQR)25

Descriptive statistics

Standard deviation24.098525
Coefficient of variation (CV)1.2937107
Kurtosis2.649303
Mean18.627444
Median Absolute Deviation (MAD)10
Skewness1.781518
Sum165747
Variance580.7389
MonotonicityNot monotonic
2024-08-12T21:02:49.833455image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0 2661
29.9%
14 377
 
4.2%
44 346
 
3.9%
22 343
 
3.9%
7 321
 
3.6%
5 299
 
3.4%
11 272
 
3.1%
6 267
 
3.0%
19 257
 
2.9%
3 252
 
2.8%
Other values (37) 3503
39.4%
ValueCountFrequency (%)
0 2661
29.9%
2 77
 
0.9%
3 252
 
2.8%
4 153
 
1.7%
5 299
 
3.4%
6 267
 
3.0%
7 321
 
3.6%
8 181
 
2.0%
9 118
 
1.3%
10 218
 
2.4%
ValueCountFrequency (%)
99 189
2.1%
98 13
 
0.1%
94 39
 
0.4%
89 1
 
< 0.1%
88 111
1.2%
81 36
 
0.4%
77 237
2.7%
63 118
1.3%
55 197
2.2%
53 5
 
0.1%
Distinct111
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size482.5 KiB
2024-08-12T21:02:50.051280image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length12
Median length10
Mean length5.9587548
Min length3

Characters and Unicode

Total characters53021
Distinct characters55
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowMika
2nd rowDavid
3rd rowMichael
4th rowRubens
5th rowHeinz-Harald
ValueCountFrequency (%)
nico 425
 
4.8%
fernando 377
 
4.2%
lewis 346
 
3.9%
kimi 321
 
3.6%
sebastian 299
 
3.4%
felipe 297
 
3.3%
sergio 272
 
3.1%
jenson 263
 
3.0%
daniel 252
 
2.8%
valtteri 237
 
2.7%
Other values (101) 5809
65.3%
2024-08-12T21:02:50.369665image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
i 5900
 
11.1%
a 5797
 
10.9%
e 5242
 
9.9%
n 4606
 
8.7%
o 3497
 
6.6%
r 3302
 
6.2%
s 2394
 
4.5%
l 2242
 
4.2%
t 1900
 
3.6%
c 1478
 
2.8%
Other values (45) 16663
31.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 43963
82.9%
Uppercase Letter 8978
 
16.9%
Dash Punctuation 80
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 5900
13.4%
a 5797
13.2%
e 5242
11.9%
n 4606
10.5%
o 3497
8.0%
r 3302
7.5%
s 2394
 
5.4%
l 2242
 
5.1%
t 1900
 
4.3%
c 1478
 
3.4%
Other values (21) 7605
17.3%
Uppercase Letter
ValueCountFrequency (%)
J 789
 
8.8%
S 765
 
8.5%
N 739
 
8.2%
M 729
 
8.1%
L 683
 
7.6%
F 681
 
7.6%
K 620
 
6.9%
R 594
 
6.6%
C 500
 
5.6%
D 477
 
5.3%
Other values (13) 2401
26.7%
Dash Punctuation
ValueCountFrequency (%)
- 80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 52941
99.8%
Common 80
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 5900
 
11.1%
a 5797
 
10.9%
e 5242
 
9.9%
n 4606
 
8.7%
o 3497
 
6.6%
r 3302
 
6.2%
s 2394
 
4.5%
l 2242
 
4.2%
t 1900
 
3.6%
c 1478
 
2.8%
Other values (44) 16583
31.3%
Common
ValueCountFrequency (%)
- 80
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 52814
99.6%
None 207
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 5900
 
11.2%
a 5797
 
11.0%
e 5242
 
9.9%
n 4606
 
8.7%
o 3497
 
6.6%
r 3302
 
6.3%
s 2394
 
4.5%
l 2242
 
4.2%
t 1900
 
3.6%
c 1478
 
2.8%
Other values (39) 16456
31.2%
None
ValueCountFrequency (%)
é 103
49.8%
É 58
28.0%
ô 40
 
19.3%
ó 4
 
1.9%
á 1
 
0.5%
Å¡ 1
 
0.5%
Distinct119
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size511.4 KiB
2024-08-12T21:02:50.594611image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length13
Median length10
Mean length7.2200494
Min length3

Characters and Unicode

Total characters64244
Distinct characters55
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)0.1%

Sample

1st rowHäkkinen
2nd rowCoulthard
3rd rowSchumacher
4th rowBarrichello
5th rowFrentzen
ValueCountFrequency (%)
alonso 377
 
4.1%
hamilton 346
 
3.7%
räikkönen 321
 
3.5%
vettel 299
 
3.2%
pérez 272
 
2.9%
schumacher 265
 
2.9%
button 263
 
2.8%
massa 257
 
2.8%
ricciardo 252
 
2.7%
bottas 237
 
2.6%
Other values (117) 6358
68.8%
2024-08-12T21:02:50.918855image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 5797
 
9.0%
a 5351
 
8.3%
o 4676
 
7.3%
n 4630
 
7.2%
l 4314
 
6.7%
i 4300
 
6.7%
r 4126
 
6.4%
s 3578
 
5.6%
t 3507
 
5.5%
c 2135
 
3.3%
Other values (45) 21830
34.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 54855
85.4%
Uppercase Letter 8992
 
14.0%
Space Separator 349
 
0.5%
Other Punctuation 48
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 5797
10.6%
a 5351
9.8%
o 4676
 
8.5%
n 4630
 
8.4%
l 4314
 
7.9%
i 4300
 
7.8%
r 4126
 
7.5%
s 3578
 
6.5%
t 3507
 
6.4%
c 2135
 
3.9%
Other values (19) 12441
22.7%
Uppercase Letter
ValueCountFrequency (%)
R 1012
11.3%
S 978
10.9%
B 826
9.2%
H 751
8.4%
M 707
7.9%
V 686
 
7.6%
P 602
 
6.7%
A 589
 
6.6%
G 583
 
6.5%
K 501
 
5.6%
Other values (13) 1757
19.5%
Other Punctuation
ValueCountFrequency (%)
. 28
58.3%
' 20
41.7%
Space Separator
ValueCountFrequency (%)
349
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 63847
99.4%
Common 397
 
0.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 5797
 
9.1%
a 5351
 
8.4%
o 4676
 
7.3%
n 4630
 
7.3%
l 4314
 
6.8%
i 4300
 
6.7%
r 4126
 
6.5%
s 3578
 
5.6%
t 3507
 
5.5%
c 2135
 
3.3%
Other values (42) 21433
33.6%
Common
ValueCountFrequency (%)
349
87.9%
. 28
 
7.1%
' 20
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63040
98.1%
None 1204
 
1.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 5797
 
9.2%
a 5351
 
8.5%
o 4676
 
7.4%
n 4630
 
7.3%
l 4314
 
6.8%
i 4300
 
6.8%
r 4126
 
6.5%
s 3578
 
5.7%
t 3507
 
5.6%
c 2135
 
3.4%
Other values (41) 20626
32.7%
None
ValueCountFrequency (%)
é 338
28.1%
ä 326
27.1%
ö 321
26.7%
ü 219
18.2%
Distinct122
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size69.6 KiB
Minimum1964-06-11 00:00:00
Maximum2005-05-08 00:00:00
2024-08-12T21:02:51.046092image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:51.183384image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Nationality
Categorical

HIGH CORRELATION 

Distinct34
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size487.9 KiB
German
1414 
British
1158 
Spanish
691 
Finnish
681 
French
676 
Other values (29)
4278 

Length

Max length13
Median length10
Mean length7.1329512
Min length4

Characters and Unicode

Total characters63469
Distinct characters40
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowFinnish
2nd rowBritish
3rd rowGerman
4th rowBrazilian
5th rowGerman

Common Values

ValueCountFrequency (%)
German 1414
15.9%
British 1158
13.0%
Spanish 691
 
7.8%
Finnish 681
 
7.7%
French 676
 
7.6%
Brazilian 623
 
7.0%
Australian 490
 
5.5%
Italian 472
 
5.3%
Mexican 331
 
3.7%
Dutch 307
 
3.5%
Other values (24) 2055
23.1%

Length

2024-08-12T21:02:51.313045image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
german 1414
15.8%
british 1158
13.0%
spanish 691
 
7.7%
finnish 681
 
7.6%
french 676
 
7.6%
brazilian 623
 
7.0%
australian 490
 
5.5%
italian 472
 
5.3%
mexican 331
 
3.7%
dutch 307
 
3.4%
Other values (25) 2085
23.4%

Most occurring characters

ValueCountFrequency (%)
i 8368
13.2%
a 8195
12.9%
n 8195
12.9%
r 4617
 
7.3%
s 4571
 
7.2%
e 4098
 
6.5%
h 4057
 
6.4%
t 2551
 
4.0%
l 1939
 
3.1%
B 1843
 
2.9%
Other values (30) 15035
23.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 54511
85.9%
Uppercase Letter 8928
 
14.1%
Space Separator 30
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 8368
15.4%
a 8195
15.0%
n 8195
15.0%
r 4617
8.5%
s 4571
8.4%
e 4098
7.5%
h 4057
7.4%
t 2551
 
4.7%
l 1939
 
3.6%
m 1546
 
2.8%
Other values (12) 6374
11.7%
Uppercase Letter
ValueCountFrequency (%)
B 1843
20.6%
G 1414
15.8%
F 1357
15.2%
S 842
9.4%
A 645
 
7.2%
I 556
 
6.2%
D 489
 
5.5%
M 473
 
5.3%
C 398
 
4.5%
J 292
 
3.3%
Other values (7) 619
 
6.9%
Space Separator
ValueCountFrequency (%)
30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 63439
> 99.9%
Common 30
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 8368
13.2%
a 8195
12.9%
n 8195
12.9%
r 4617
 
7.3%
s 4571
 
7.2%
e 4098
 
6.5%
h 4057
 
6.4%
t 2551
 
4.0%
l 1939
 
3.1%
B 1843
 
2.9%
Other values (29) 15005
23.7%
Common
ValueCountFrequency (%)
30
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63469
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 8368
13.2%
a 8195
12.9%
n 8195
12.9%
r 4617
 
7.3%
s 4571
 
7.2%
e 4098
 
6.5%
h 4057
 
6.4%
t 2551
 
4.0%
l 1939
 
3.1%
B 1843
 
2.9%
Other values (30) 15035
23.7%

ConstructorID
Categorical

HIGH CORRELATION 

Distinct38
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size490.8 KiB
ferrari
850 
williams
848 
mclaren
847 
red_bull
766 
mercedes
589 
Other values (33)
4998 

Length

Max length12
Median length11
Mean length7.4678579
Min length2

Characters and Unicode

Total characters66449
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmclaren
2nd rowmclaren
3rd rowferrari
4th rowferrari
5th rowjordan

Common Values

ValueCountFrequency (%)
ferrari 850
 
9.6%
williams 848
 
9.5%
mclaren 847
 
9.5%
red_bull 766
 
8.6%
mercedes 589
 
6.6%
toro_rosso 533
 
6.0%
renault 522
 
5.9%
sauber 499
 
5.6%
force_india 423
 
4.8%
haas 359
 
4.0%
Other values (28) 2662
29.9%

Length

2024-08-12T21:02:51.424747image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ferrari 850
 
9.6%
williams 848
 
9.5%
mclaren 847
 
9.5%
red_bull 766
 
8.6%
mercedes 589
 
6.6%
toro_rosso 533
 
6.0%
renault 522
 
5.9%
sauber 499
 
5.6%
force_india 423
 
4.8%
haas 359
 
4.0%
Other values (28) 2662
29.9%

Most occurring characters

ValueCountFrequency (%)
r 9099
13.7%
a 7917
11.9%
e 6226
 
9.4%
l 5360
 
8.1%
i 4694
 
7.1%
s 4269
 
6.4%
o 3845
 
5.8%
n 3042
 
4.6%
m 3036
 
4.6%
u 2679
 
4.0%
Other values (15) 16282
24.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 63845
96.1%
Connector Punctuation 2414
 
3.6%
Decimal Number 190
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 9099
14.3%
a 7917
12.4%
e 6226
9.8%
l 5360
 
8.4%
i 4694
 
7.4%
s 4269
 
6.7%
o 3845
 
6.0%
n 3042
 
4.8%
m 3036
 
4.8%
u 2679
 
4.2%
Other values (13) 13678
21.4%
Connector Punctuation
ValueCountFrequency (%)
_ 2414
100.0%
Decimal Number
ValueCountFrequency (%)
1 190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 63845
96.1%
Common 2604
 
3.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 9099
14.3%
a 7917
12.4%
e 6226
9.8%
l 5360
 
8.4%
i 4694
 
7.4%
s 4269
 
6.7%
o 3845
 
6.0%
n 3042
 
4.8%
m 3036
 
4.8%
u 2679
 
4.2%
Other values (13) 13678
21.4%
Common
ValueCountFrequency (%)
_ 2414
92.7%
1 190
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66449
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 9099
13.7%
a 7917
11.9%
e 6226
 
9.4%
l 5360
 
8.1%
i 4694
 
7.1%
s 4269
 
6.4%
o 3845
 
5.8%
n 3042
 
4.6%
m 3036
 
4.6%
u 2679
 
4.0%
Other values (15) 16282
24.5%

ConstructorName
Categorical

HIGH CORRELATION 

Distinct38
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size496.4 KiB
Ferrari
850 
Williams
848 
McLaren
847 
Red Bull
766 
Mercedes
589 
Other values (33)
4998 

Length

Max length14
Median length12
Mean length8.1062036
Min length3

Characters and Unicode

Total characters72129
Distinct characters38
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMcLaren
2nd rowMcLaren
3rd rowFerrari
4th rowFerrari
5th rowJordan

Common Values

ValueCountFrequency (%)
Ferrari 850
 
9.6%
Williams 848
 
9.5%
McLaren 847
 
9.5%
Red Bull 766
 
8.6%
Mercedes 589
 
6.6%
Toro Rosso 533
 
6.0%
Renault 522
 
5.9%
Sauber 499
 
5.6%
Force India 423
 
4.8%
Haas F1 Team 359
 
4.0%
Other values (28) 2662
29.9%

Length

2024-08-12T21:02:51.535712image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ferrari 850
 
6.7%
williams 848
 
6.7%
mclaren 847
 
6.7%
red 766
 
6.1%
bull 766
 
6.1%
f1 701
 
5.6%
sauber 639
 
5.1%
mercedes 589
 
4.7%
team 547
 
4.3%
toro 533
 
4.2%
Other values (35) 5517
43.8%

Most occurring characters

ValueCountFrequency (%)
a 7621
 
10.6%
e 6980
 
9.7%
r 6933
 
9.6%
l 4283
 
5.9%
i 4261
 
5.9%
o 4259
 
5.9%
3705
 
5.1%
s 3642
 
5.0%
n 2966
 
4.1%
u 2745
 
3.8%
Other values (28) 24734
34.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 53265
73.8%
Uppercase Letter 14422
 
20.0%
Space Separator 3705
 
5.1%
Decimal Number 737
 
1.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 7621
14.3%
e 6980
13.1%
r 6933
13.0%
l 4283
8.0%
i 4261
8.0%
o 4259
8.0%
s 3642
6.8%
n 2966
 
5.6%
u 2745
 
5.2%
d 2124
 
4.0%
Other values (11) 7451
14.0%
Uppercase Letter
ValueCountFrequency (%)
R 2363
16.4%
M 2142
14.9%
F 2010
13.9%
T 1611
11.2%
B 1094
7.6%
L 1077
7.5%
W 988
6.9%
A 899
 
6.2%
S 759
 
5.3%
H 580
 
4.0%
Other values (5) 899
 
6.2%
Space Separator
ValueCountFrequency (%)
3705
100.0%
Decimal Number
ValueCountFrequency (%)
1 737
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 67687
93.8%
Common 4442
 
6.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 7621
 
11.3%
e 6980
 
10.3%
r 6933
 
10.2%
l 4283
 
6.3%
i 4261
 
6.3%
o 4259
 
6.3%
s 3642
 
5.4%
n 2966
 
4.4%
u 2745
 
4.1%
R 2363
 
3.5%
Other values (26) 21634
32.0%
Common
ValueCountFrequency (%)
3705
83.4%
1 737
 
16.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72129
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 7621
 
10.6%
e 6980
 
9.7%
r 6933
 
9.6%
l 4283
 
5.9%
i 4261
 
5.9%
o 4259
 
5.9%
3705
 
5.1%
s 3642
 
5.0%
n 2966
 
4.1%
u 2745
 
3.8%
Other values (28) 24734
34.3%

ConstructorNationality
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size485.1 KiB
British
2471 
Italian
1707 
Austrian
766 
German
729 
Swiss
706 
Other values (9)
2519 

Length

Max length9
Median length8
Mean length6.815127
Min length5

Characters and Unicode

Total characters60641
Distinct characters26
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBritish
2nd rowBritish
3rd rowItalian
4th rowItalian
5th rowIrish

Common Values

ValueCountFrequency (%)
British 2471
27.8%
Italian 1707
19.2%
Austrian 766
 
8.6%
German 729
 
8.2%
Swiss 706
 
7.9%
French 692
 
7.8%
Japanese 434
 
4.9%
Indian 423
 
4.8%
American 359
 
4.0%
Malaysian 188
 
2.1%
Other values (4) 423
 
4.8%

Length

2024-08-12T21:02:51.655009image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
british 2471
27.8%
italian 1707
19.2%
austrian 766
 
8.6%
german 729
 
8.2%
swiss 706
 
7.9%
french 692
 
7.8%
japanese 434
 
4.9%
indian 423
 
4.8%
american 359
 
4.0%
malaysian 188
 
2.1%
Other values (4) 423
 
4.8%

Most occurring characters

ValueCountFrequency (%)
i 9472
15.6%
a 7384
12.2%
n 5982
9.9%
s 5798
9.6%
r 5137
8.5%
t 4986
8.2%
h 3440
 
5.7%
e 2648
 
4.4%
B 2471
 
4.1%
I 2250
 
3.7%
Other values (16) 11073
18.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 51743
85.3%
Uppercase Letter 8898
 
14.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 9472
18.3%
a 7384
14.3%
n 5982
11.6%
s 5798
11.2%
r 5137
9.9%
t 4986
9.6%
h 3440
 
6.6%
e 2648
 
5.1%
l 1895
 
3.7%
c 1093
 
2.1%
Other values (6) 3908
7.6%
Uppercase Letter
ValueCountFrequency (%)
B 2471
27.8%
I 2250
25.3%
A 1125
12.6%
S 821
 
9.2%
G 729
 
8.2%
F 692
 
7.8%
J 434
 
4.9%
M 188
 
2.1%
R 146
 
1.6%
D 42
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 60641
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 9472
15.6%
a 7384
12.2%
n 5982
9.9%
s 5798
9.6%
r 5137
8.5%
t 4986
8.2%
h 3440
 
5.7%
e 2648
 
4.4%
B 2471
 
4.1%
I 2250
 
3.7%
Other values (16) 11073
18.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60641
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 9472
15.6%
a 7384
12.2%
n 5982
9.9%
s 5798
9.6%
r 5137
8.5%
t 4986
8.2%
h 3440
 
5.7%
e 2648
 
4.4%
B 2471
 
4.1%
I 2250
 
3.7%
Other values (16) 11073
18.3%

Q1
Text

Distinct7916
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Memory size494.6 KiB
2024-08-12T21:02:51.892379image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.9040234
Min length1

Characters and Unicode

Total characters70330
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7111 ?
Unique (%)79.9%

Sample

1st row1:30.556
2nd row1:30.910
3rd row1:31.075
4th row1:31.102
5th row1:31.359
ValueCountFrequency (%)
0 122
 
1.4%
1:17.244 4
 
< 0.1%
1:22.043 3
 
< 0.1%
1:15.644 3
 
< 0.1%
1:17.086 3
 
< 0.1%
1:22.130 3
 
< 0.1%
1:23.578 3
 
< 0.1%
1:27.039 3
 
< 0.1%
1:15.746 3
 
< 0.1%
1:25.859 3
 
< 0.1%
Other values (7906) 8748
98.3%
2024-08-12T21:02:52.244396image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 14420
20.5%
: 8776
12.5%
. 8776
12.5%
2 5933
8.4%
3 5920
8.4%
4 4620
 
6.6%
5 3971
 
5.6%
0 3905
 
5.6%
6 3644
 
5.2%
7 3536
 
5.0%
Other values (2) 6829
9.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 52778
75.0%
Other Punctuation 17552
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 14420
27.3%
2 5933
11.2%
3 5920
11.2%
4 4620
 
8.8%
5 3971
 
7.5%
0 3905
 
7.4%
6 3644
 
6.9%
7 3536
 
6.7%
8 3505
 
6.6%
9 3324
 
6.3%
Other Punctuation
ValueCountFrequency (%)
: 8776
50.0%
. 8776
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 70330
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 14420
20.5%
: 8776
12.5%
. 8776
12.5%
2 5933
8.4%
3 5920
8.4%
4 4620
 
6.6%
5 3971
 
5.6%
0 3905
 
5.6%
6 3644
 
5.2%
7 3536
 
5.0%
Other values (2) 6829
9.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70330
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 14420
20.5%
: 8776
12.5%
. 8776
12.5%
2 5933
8.4%
3 5920
8.4%
4 4620
 
6.6%
5 3971
 
5.6%
0 3905
 
5.6%
6 3644
 
5.2%
7 3536
 
5.0%
Other values (2) 6829
9.7%

Q2
Text

Distinct5345
Distinct (%)60.1%
Missing0
Missing (%)0.0%
Memory size473.6 KiB
2024-08-12T21:02:52.489751image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length8
Median length8
Mean length5.483367
Min length1

Characters and Unicode

Total characters48791
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5011 ?
Unique (%)56.3%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 3199
36.0%
1:26.319 3
 
< 0.1%
1:17.166 3
 
< 0.1%
1:37.347 3
 
< 0.1%
1:38.417 3
 
< 0.1%
1:15.885 3
 
< 0.1%
1:33.416 3
 
< 0.1%
1:15.322 3
 
< 0.1%
1:31.010 3
 
< 0.1%
1:15.706 3
 
< 0.1%
Other values (5335) 5672
63.7%
2024-08-12T21:02:52.855095image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 9385
19.2%
0 5727
11.7%
: 5699
11.7%
. 5699
11.7%
2 3919
8.0%
3 3751
 
7.7%
4 2969
 
6.1%
5 2630
 
5.4%
6 2377
 
4.9%
7 2336
 
4.8%
Other values (2) 4299
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37393
76.6%
Other Punctuation 11398
 
23.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9385
25.1%
0 5727
15.3%
2 3919
10.5%
3 3751
 
10.0%
4 2969
 
7.9%
5 2630
 
7.0%
6 2377
 
6.4%
7 2336
 
6.2%
8 2212
 
5.9%
9 2087
 
5.6%
Other Punctuation
ValueCountFrequency (%)
: 5699
50.0%
. 5699
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 48791
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9385
19.2%
0 5727
11.7%
: 5699
11.7%
. 5699
11.7%
2 3919
8.0%
3 3751
 
7.7%
4 2969
 
6.1%
5 2630
 
5.4%
6 2377
 
4.9%
7 2336
 
4.8%
Other values (2) 4299
8.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 48791
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9385
19.2%
0 5727
11.7%
: 5699
11.7%
. 5699
11.7%
2 3919
8.0%
3 3751
 
7.7%
4 2969
 
6.1%
5 2630
 
5.4%
6 2377
 
4.9%
7 2336
 
4.8%
Other values (2) 4299
8.8%

Q3
Text

Distinct3392
Distinct (%)38.1%
Missing0
Missing (%)0.0%
Memory size458.8 KiB
2024-08-12T21:02:53.095453image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Length

Max length8
Median length1
Mean length3.7801753
Min length1

Characters and Unicode

Total characters33636
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3253 ?
Unique (%)36.6%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 5364
60.3%
1:14.970 3
 
< 0.1%
1:35.766 3
 
< 0.1%
1:45.503 3
 
< 0.1%
1:38.513 3
 
< 0.1%
1:31.478 3
 
< 0.1%
1:24.305 2
 
< 0.1%
1:26.973 2
 
< 0.1%
1:16.818 2
 
< 0.1%
1:47.362 2
 
< 0.1%
Other values (3382) 3511
39.5%
2024-08-12T21:02:53.459568image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 6968
20.7%
1 5885
17.5%
: 3534
10.5%
. 3534
10.5%
2 2474
 
7.4%
3 2242
 
6.7%
4 1872
 
5.6%
5 1555
 
4.6%
7 1427
 
4.2%
6 1400
 
4.2%
Other values (2) 2745
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 26568
79.0%
Other Punctuation 7068
 
21.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6968
26.2%
1 5885
22.2%
2 2474
 
9.3%
3 2242
 
8.4%
4 1872
 
7.0%
5 1555
 
5.9%
7 1427
 
5.4%
6 1400
 
5.3%
8 1373
 
5.2%
9 1372
 
5.2%
Other Punctuation
ValueCountFrequency (%)
: 3534
50.0%
. 3534
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 33636
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6968
20.7%
1 5885
17.5%
: 3534
10.5%
. 3534
10.5%
2 2474
 
7.4%
3 2242
 
6.7%
4 1872
 
5.6%
5 1555
 
4.6%
7 1427
 
4.2%
6 1400
 
4.2%
Other values (2) 2745
 
8.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33636
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6968
20.7%
1 5885
17.5%
: 3534
10.5%
. 3534
10.5%
2 2474
 
7.4%
3 2242
 
6.7%
4 1872
 
5.6%
5 1555
 
4.6%
7 1427
 
4.2%
6 1400
 
4.2%
Other values (2) 2745
 
8.2%

Interactions

2024-08-12T21:02:46.674309image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:45.437902image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:45.808577image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:46.140736image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:46.766066image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:45.524728image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:45.894522image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:46.423979image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:46.865797image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:45.615571image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:45.970192image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:46.504762image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:46.950930image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:45.722789image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:46.055972image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
2024-08-12T21:02:46.586544image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/

Correlations

2024-08-12T21:02:53.551322image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
CircuitIDConstructorIDConstructorNameConstructorNationalityNationalityPermanentNumberPositionRoundSeason
CircuitID1.0000.0540.0540.0490.0000.0720.0000.6590.285
ConstructorID0.0541.0001.0000.9990.3850.4080.3590.0140.432
ConstructorName0.0541.0001.0000.9990.3850.4080.3590.0140.432
ConstructorNationality0.0490.9990.9991.0000.4060.2950.2930.0000.244
Nationality0.0000.3850.3850.4061.0000.5810.2010.0000.281
PermanentNumber0.0720.4080.4080.2950.5811.000-0.1580.0560.576
Position0.0000.3590.3590.2930.201-0.1581.000-0.004-0.036
Round0.6590.0140.0140.0000.0000.056-0.0041.0000.083
Season0.2850.4320.4320.2440.2810.576-0.0360.0831.000

Missing values

2024-08-12T21:02:47.084081image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-08-12T21:02:47.319685image/svg+xmlMatplotlib v3.9.1.post1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SeasonRoundCircuitIDPositionDriverIDCodePermanentNumberGivenNameFamilyNameDateOfBirthNationalityConstructorIDConstructorNameConstructorNationalityQ1Q2Q3
020001albert_park1hakkinenNaN0MikaHäkkinen1968-09-28FinnishmclarenMcLarenBritish1:30.55600
120001albert_park2coulthardCOU0DavidCoulthard1971-03-27BritishmclarenMcLarenBritish1:30.91000
220001albert_park3michael_schumacherMSC0MichaelSchumacher1969-01-03GermanferrariFerrariItalian1:31.07500
320001albert_park4barrichelloBAR0RubensBarrichello1972-05-23BrazilianferrariFerrariItalian1:31.10200
420001albert_park5frentzenNaN0Heinz-HaraldFrentzen1967-05-18GermanjordanJordanIrish1:31.35900
520001albert_park6trulliTRU0JarnoTrulli1974-07-13ItalianjordanJordanIrish1:31.50400
620001albert_park7irvineNaN0EddieIrvine1965-11-10BritishjaguarJaguarBritish1:31.51400
720001albert_park8villeneuveVIL0JacquesVilleneuve1971-04-09CanadianbarBARBritish1:31.96800
820001albert_park9fisichellaFIS0GiancarloFisichella1973-01-14ItalianbenettonBenettonItalian1:31.99200
920001albert_park10saloNaN0MikaSalo1966-11-30FinnishsauberSauberSwiss1:32.01800
SeasonRoundCircuitIDPositionDriverIDCodePermanentNumberGivenNameFamilyNameDateOfBirthNationalityConstructorIDConstructorNameConstructorNationalityQ1Q2Q3
8888202414spa11albonALB23AlexanderAlbon1996-03-23ThaiwilliamsWilliamsBritish1:55.7221:54.4730
8889202414spa12gaslyGAS10PierreGasly1996-02-07FrenchalpineAlpine F1 TeamFrench1:54.9111:54.6350
8890202414spa13ricciardoRIC3DanielRicciardo1989-07-01AustralianrbRB F1 TeamItalian1:55.4511:54.6820
8891202414spa14bottasBOT77ValtteriBottas1989-08-28FinnishsauberSauberSwiss1:55.5311:54.7640
8892202414spa15strollSTR18LanceStroll1998-10-29Canadianaston_martinAston MartinBritish1:56.0721:55.7160
8893202414spa16hulkenbergHUL27NicoHülkenberg1987-08-19GermanhaasHaas F1 TeamAmerican1:56.30800
8894202414spa17kevin_magnussenMAG20KevinMagnussen1992-10-05DanishhaasHaas F1 TeamAmerican1:56.50000
8895202414spa18tsunodaTSU22YukiTsunoda2000-05-11JapaneserbRB F1 TeamItalian1:56.59300
8896202414spa19sargeantSAR2LoganSargeant2000-12-31AmericanwilliamsWilliamsBritish1:57.23000
8897202414spa20zhouZHO24GuanyuZhou1999-05-30ChinesesauberSauberSwiss1:57.77500